If you have tried to scale a Product Carbon Footprint program, you already know the calculation itself is rarely the main problem.
The harder challenge is turning supplier data into something that can support product-level decisions.
As customer and regulatory expectations change, that gap is becoming more visible. A few years ago, broad model-level estimates were often enough. Increasingly, manufacturers are being asked for configuration-specific Product Carbon Footprints (PCFs) tied to actual materials, suppliers, components, and manufacturing routes.
Many companies already collect carbon data from suppliers, but it often arrives disconnected from the product structure it is supposed to describe.
One supplier sends a spreadsheet. Another sends a PDF. One provides company-level emissions data when the request was product-specific. Another shares a number without a clear methodology, boundary, region, or reporting year. Some suppliers respond late. Some do not respond at all.
At that point, the challenge is no longer just emissions calculation — it becomes a data structure problem.
Supplier carbon data only becomes useful when it connects to the product
A PCF depends on more than a single emissions value. It depends on how materials, components, suppliers, manufacturing processes, transport, and energy use connect to the product itself.
That sounds straightforward in theory, but in practice supplier data often sits outside the systems used to manage products.
As a result, sustainability teams spend time interpreting inputs before they can use them. Does this figure apply to a material, a component, a supplier site, or broader company operations? Is it current? Can it be reused across product variants? Was the methodology comparable to the previous supplier submission?
Those judgement calls determine whether a PCF can be trusted.
This is one reason many PCF programs become difficult to scale. The issue is not simply collecting supplier data. It is maintaining enough structure around that data for teams to reuse it consistently across products, suppliers, and reporting cycles.
The spreadsheet problem is really a product model problem
Spreadsheets are not inherently the issue. Most companies start there because spreadsheets are easy to distribute across supply chains.
The problem emerges once PCFs move beyond a pilot exercise.
A manufacturer preparing PCFs for enterprise tenders may need footprints for multiple configurations of the same product, each with different suppliers, components, materials, or manufacturing locations. If supplier carbon data sits in disconnected files, teams are forced to manually determine which values apply to which configuration, often under commercial deadlines.
That creates operational fragility.
A supplier update may affect dozens of products. A component may appear across multiple product families. A methodology change may alter previously published values. Without a connected product model, those dependencies become difficult to track reliably.
Lenovo’s ThinkPad line illustrates how this changes once supplier and component data are tied directly to the product structure. Enterprise customers increasingly required configuration-specific, ISO-aligned PCFs rather than broad model-level estimates. In response, and with the help of Makersite, Lenovo built a configuration-level modelling approach using primary supplier data and audited methodology.
Lenovo has now structured more than 2.5 million supplier FMDs into a shared component foundation that can be reused across product families. That shifts PCF generation away from rebuilding calculations product by product and toward a more repeatable modelling approach tied to actual product configurations.
Sustainability teams cannot spend all their time interpreting supplier files
When supplier data remains disconnected from the product model, sustainability teams end up acting as translators between spreadsheets, supplier submissions, engineering structures, and reporting requirements.
Some of that work is unavoidable, but too often specialist time gets consumed by checking files, reconciling assumptions, validating formats, and explaining why one supplier submission can or cannot be used.
That has practical consequences beyond reporting. If PCFs are needed for customer tenders, they have to be available before commercial decisions are made. If procurement teams want to compare suppliers, the underlying data has to support like-for-like analysis. If engineering teams want to reduce product impact, the footprint has to connect early enough to influence design decisions.
A PCF generated after the fact supports reporting. A PCF connected to the product model can support decisions.
The next bottleneck is data exchange between companies
Internal product modelling is only part of the challenge. Supplier PCF data also has to move between manufacturers, suppliers, and broader supply chain networks. In many industries, that exchange still happens through spreadsheets, PDFs, emails, and custom templates.
That creates another scaling problem.
Even when suppliers provide carbon data, manufacturers still need a reliable way to exchange, validate, and interpret it across systems, regions, and reporting frameworks.
This is part of the reason industry-led exchange frameworks have gained momentum. Manufacturers increasingly need product-level carbon data that can move across company boundaries without requiring every supplier to work inside the same platform.
SiGREEN, which Makersite announced it will acquire effective June 2026, was designed around that exchange problem. Siemens developed the platform to support the collection and exchange of verified Product Carbon Footprint data across supply chains. It currently powers the Together for Sustainability (TfS) PCF Exchange and connects frameworks including TfS, Catena-X, and PACT.
Structured exchange reduces friction between companies, but exchange alone is not enough.
Exchanged data still needs to connect to product decisions
Supplier PCF data only becomes operationally useful once it connects back to the wider product structure inside the manufacturer. That includes materials, components, suppliers, manufacturing processes, regions, methodologies, cost structures, and regulatory requirements already managed across PLM, ERP, and supply chain systems.
Without that connection, carbon data may move more efficiently between companies while still remaining disconnected from the decisions it is meant to support.
This is where the problem shifts from data exchange to product intelligence.
Connecting supplier carbon data to the wider product model makes it easier to understand where supplier changes affect multiple products, where components can be reused across product families, and where footprint assumptions influence commercial, engineering, or compliance decisions elsewhere in the portfolio.
At that point, a PCF stops behaving like a one-time reporting output and starts becoming part of the operational product data manufacturers use for decisions.
Scaling PCFs depends on data flow, not more templates
Most manufacturers do not lack supplier carbon data entirely. What they often lack is a reliable way to structure, connect, and reuse that data across products, suppliers, and decisions.
That is why scaling PCFs is becoming less about calculation methodology alone and more about how product and supplier data move through the organisation.
The companies that scale PCFs successfully will not necessarily be the ones collecting the most spreadsheets. They will be the ones that connect supplier data directly to the product decisions it is meant to inform.
